BJPS blog – Causation in Scientific Methods

Need scientists worry about philosophy? Or should philosophers get off their backs and let them do their work in peace? Unsurprisingly, many scientists want to stay clear of philosophical discussions. What is more disturbing is when I hear philosophers themselves announce that our discipline has nothing useful to offer science. In my view, they could not be more wrong.

This blog post was written for BJPS‘s Auxiliary Hypotheses – Stories from the world of academic philosophy of science. Read full text here.

When a cause cannot be found

There is a philosophical problem within medicine: how to deal with causal complexity and variations. While existing methods are designed for large scale population data and sufficiently homogenous sub-groups, a number of medical conditions are characterised by their heterogenic and complex nature: low back pain (LBP), chronic fatigue syndrome (CFS), fibromyalgia syndrome (FMS), irritable bowel syndrome (IBS), tension-type headache (TTH), post-traumatic stress disorder (PTSD), and many others.

The causal value of Big Data. Two views on causation

Not everything should be approached quantitatively. Is causation perhaps one of them?

Scientific research relies on data, and preferably lots of it. Population studies and statistical models are used to find and establish causal knowledge. The idea is that the more data we have, the better justification we have for our causal hypotheses.

One cause, different effects

Often we behave as if we think that a cause can only have one effect, while in fact it can have many. The type of effect depends on what else there is, besides the cause. In other words, it’s a matter of context.